Investigação Operacional

The teaching methodologies adopted in this curricular unit are based on the articulation between theoretical exposition, guided experimentation, and autonomous learning, ensuring coherence with the learning objectives and with a student-centered pedagogical model. In the theoretical component, oral presentation of the contents, supported by the board and multimedia projections, provides the fundamental conceptual structure and promotes understanding of Operations Research methods. Active participation is encouraged through guiding questions that stimulate reasoning and the gradual construction of knowledge.
In the theoretical-practical component, students apply the concepts acquired to the resolution of simplified or simulated real-world problems, mobilizing skills in mathematical modelling, algorithm selection, and interpretation of results. The exercise sheets provided support systematic and guided work, allowing students to develop autonomy and critical thinking. This approach reinforces a pedagogical model based on learning-by-doing, the development of analytical competences, and the ability to transfer knowledge to new contexts.
Individual study is also valued as an essential element for consolidating learning, enabling students to deepen content, review solution methods, and prepare for active participation in class. The integration of theory, practice, and autonomous study ensures a coherent, continuous, and progressive learning process, aligned with the principles of higher education focused on the development of applied competences and problem-solving skills.

Students develop the ability to translate simple real-world problems into mathematical Linear Programming models, apply Operations Research methods to solve these models, and critically interpret the solutions obtained. They are also encouraged to identify different types of optimization and decision problems, understand the modelling process, select appropriate algorithms, and analyse the sensitivity of solutions to parameter variations. These learning objectives align with the teaching methodology, which combines theoretical exposition with guided problem-solving, promoting experimentation, practical application of concepts, and active student participation, supported by complementary individual study.

The course covers the foundational concepts of Operations Research, including its purpose, origins, and areas of application. It then develops the study of Linear Programming, addressing model formulation, solution methods, and interpretation of results. The program introduces post-optimization and sensitivity analysis, enabling the assessment of how parameter variations affect optimal solutions. It also explores Multi-Objective Linear Programming and Goal Programming, focusing on modelling and solving problems with multiple criteria.
Theoretical content:
1 – Introduction to Operations Research
2 – Linear Programming
3 – Introduction to Post-Optimization and Sensitivity Analysis
4 -Multi-Objective Linear Programming
5 – Goal Programming
Theoretical-Practical content:
Includes the guided resolution of exercises related to all topics covered in the theoretical component.

• - Tests (2) or Exam - 100.0%

NAO